Manager, Engineering (Data Platform)

Garner Health•New York, NY

16h•$239,000 - $275,000•Hybrid

About The Position

Garner is seeking an execution-focused and visionary Data Engineering Manager to lead their Data Platform Team. This role is critical for building common, reusable abstractions and high-throughput data infrastructure that power Garner’s internal and external applications. The manager will champion an AI-native development philosophy and scale data pipeline capabilities to support the company's growth. The position is based in the New York City office and requires working in the office 3 days per week (Tuesday, Wednesday, and Thursday).

Requirements

5+ years of hands-on engineering experience, with a focus on Big Data and distributed system and infrastructure.
2+ years of managing engineering teams, with a track record of building and mentoring high performing teams.
Expertise with batch data processing frameworks such as Hadoop, Spark, EMR, etc.
Experience with stream processing systems such as Kafka and RedPanda.
Proficiency with technologies such as Temporal, Apache iceberg, AWS glue, Spark, DBT, and Soda.
Exhibits strong ownership and a proactive mindset, with a proven ability to translate complex business needs into scalable, actionable technical roadmaps.
Demonstrated ability to operate effectively in dynamic, fast-paced startup environments, with a hands-on approach and a strong commitment to organizational success.

Nice To Haves

Preference for managing healthcare data standards (e.g. HIPAA, EDI).
A desire to be a part of our mission to improve the U.S. healthcare system.
A desire to be a part of a high-performing, mission-driven team that operates with intense urgency, a strong sense of individual accountability, and a commitment to authentic feedback.

Responsibilities

Lead and manage the Data Platform Engineering team, owning the end-to-end design, implementation, and operational excellence of systems that process complex healthcare data.
Provide high-quality technical contributions and architectural guidance, establishing robust standards for data modeling, processing, and system maintainability.
Build and optimize common data abstractions, pipelines, and frameworks designed for high scalability, ensuring the platform keeps pace with exponential enterprise data growth.
Translate complex, ambiguous business and data requirements into well-scoped, accurately estimated, and composable architectural pieces.
Foster and drive an AI-native engineering culture within the team, integrating cutting-edge AI tools and integrations to enhance development workflows and automate data operations.
Manage and grow stakeholder relationships across Engineering and Product, aligning team priorities with departmental OKRs and broader business goals.
Mentor, coach, and guide team members, conducting detailed design reviews and managing robust Root Cause Analyses (RCA) to continuously elevate technical rigor.